diff --git a/litellm/llms/huggingface_restapi.py b/litellm/llms/huggingface_restapi.py
index 43c293a7b4..de1781ee0d 100644
--- a/litellm/llms/huggingface_restapi.py
+++ b/litellm/llms/huggingface_restapi.py
@@ -102,8 +102,7 @@ def completion(
                 "stream": True if "stream" in optional_params and optional_params["stream"] == True else False,
             }
         input_text = prompt
-    elif task == "other":
-        print("task=other, custom api base")
+    elif task == "other" or task == None:
         if model in custom_prompt_dict:
             # check if the model has a registered custom prompt
             model_prompt_details = custom_prompt_dict[model]
@@ -118,8 +117,6 @@ def completion(
         inference_params = copy.deepcopy(optional_params)
         inference_params.pop("details")
         inference_params.pop("return_full_text")
-        print("inf params")
-        print(inference_params)
         data = {
             "inputs": prompt,
             "parameters": inference_params,
@@ -185,8 +182,8 @@ def completion(
                     for token in completion_response[0]["details"]["tokens"]:
                         sum_logprob += token["logprob"]
                     model_response["choices"][0]["message"]["logprobs"] = sum_logprob
-            elif task == "other":
-                model_response["choices"][0]["message"]["content"] = str(completion_response[0]["generated_text"])
+            else:
+                model_response["choices"][0]["message"]["content"] = completion_response[0]["generated_text"]
         ## CALCULATING USAGE
         prompt_tokens = len(
             encoding.encode(input_text)